Module 02: Reactive Programming
School of Mathematical and Physical Sciences
2024-09-28
renderText() action every time we update input$name (automatically!)Recipe
renderText({}) informs Shiny how it could create the string if it needs to, but it’s up to Shiny when (and even if!) the code should be runoutput$greeting will need to be recomputed whenever input$name is changedgreeting has a reactive dependency on name
Reactive functions
Shiny provides a variety of reactive functions such as reactive(), observe(), bindevent() , render*(), etc.
demos/demo01.R
library(tidyverse)
library(shiny)
d = readr::read_csv(here::here("data/weather.csv"))
ui = fluidPage(
titlePanel("Temperatures at Major Airports"),
sidebarLayout(
sidebarPanel(
radioButtons(
"name", "Select an airport",
choices = c(
"Seattle-Tacoma",
"Raleigh-Durham",
"Houston Intercontinental",
"Denver",
"Los Angeles",
"John F. Kennedy"
)
)
),
mainPanel(
plotOutput("plot")
)
)
)
server = function(input, output, session) {
output$plot = renderPlot({
d |>
filter(name %in% input$name) |>
ggplot(aes(x=date, y=temp_avg)) +
geom_line() +
theme_minimal()
})
}
shinyApp(ui = ui, server = server)Our inputs and outputs are defined by the elements in our UI.
demos/demo01.R
library(tidyverse)
library(shiny)
d = readr::read_csv(here::here("data/weather.csv"))
ui = fluidPage(
titlePanel("Temperatures at Major Airports"),
sidebarLayout(
sidebarPanel(
radioButtons(
"name", "Select an airport",
choices = c(
"Seattle-Tacoma",
"Raleigh-Durham",
"Houston Intercontinental",
"Denver",
"Los Angeles",
"John F. Kennedy"
)
)
),
mainPanel(
plotOutput("plot")
)
)
)
server = function(input, output, session) {
output$plot = renderPlot({
d |>
filter(name %in% input$name) |>
ggplot(aes(x=date, y=temp_avg)) +
geom_line() +
theme_minimal()
})
}
shinyApp(ui = ui, server = server)The “reactive” logic is defined in our server function - Shiny takes care of figuring out what depends on what.
demos/demo02.R
library(tidyverse)
library(shiny)
d = readr::read_csv(here::here("data/weather.csv"))
d_vars <- c("Average temp" = "temp_avg",
"Min temp" = "temp_min",
"Max temp" = "temp_max",
"Total precip" = "precip",
"Snow depth" = "snow",
"Wind direction" = "wind_direction",
"Wind speed" = "wind_speed",
"Air pressure" = "air_press")
ui <- fluidPage(
titlePanel("Weather Data"),
sidebarLayout(
sidebarPanel(
radioButtons(
"name", "Select an airport",
choices = c(
"Seattle-Tacoma",
"Raleigh-Durham",
"Houston Intercontinental",
"Denver",
"Los Angeles",
"John F. Kennedy"
)
),
selectInput(
"var", "Select a variable",
choices = d_vars, selected = "temp_avg"
)
),
mainPanel(
plotOutput("plot")
)
)
)
server <- function(input, output, session) {
output$plot = renderPlot({
d |>
filter(name %in% input$name) |>
ggplot(aes(x=date, y=.data[[input$var]])) +
geom_line() +
theme_minimal()
})
}
shinyApp(ui = ui, server = server)One aspect of the improved code that you might be unfamiliar with is the use of .data[[input$var]] within renderPlot().
How to create Shiny apps that lets the user choose which variables will be fed into tidyverse functions like dplyr::filter() and ggplot2::aes().
There is a problem of indirection - we have a data-variable stored inside an env-variable (input$var)
<-..data[[input$var]] is a way to tell tidyverse functions to look inside the data frame for a variable whose name is stored in input$var.
With these additions, what should our reactive graph look like now?
Starting with the code in exercises/ex03.R (based on demo02.R’s code) add a tableOutput() with id minmax to the app’s mainPanel().
Once you have done that you should then add logic to the server function to render a table that shows the min and max temperature for each year contained in these data.
ui
server function to generate these summaries.lubridate::year() will be useful along with dplyr::group_by() and dplyr::summarize().12:00
With these additions, what should our reactive graph look like now?
Another (more detailed) way of seeing the reactive graph (dynamically) for your app is using the reactlog package.
Run the following to log and show all of the reactive events occurring within ex03_soln.R,
demos/demo03.R
library(tidyverse)
library(shiny)
d = readr::read_csv(here::here("data/weather.csv"))
d_vars = c("Average temp" = "temp_avg",
"Min temp" = "temp_min",
"Max temp" = "temp_max",
"Total precip" = "precip",
"Snow depth" = "snow",
"Wind direction" = "wind_direction",
"Wind speed" = "wind_speed",
"Air pressure" = "air_press",
"Total sunshine" = "total_sun")
ui = fluidPage(
titlePanel("Weather Data"),
sidebarLayout(
sidebarPanel(
radioButtons(
"name", "Select an airport",
choices = c(
"Raleigh-Durham",
"Houston Intercontinental",
"Denver",
"Los Angeles",
"John F. Kennedy"
)
),
selectInput(
"var", "Select a variable",
choices = d_vars, selected = "temp_avg"
)
),
mainPanel(
plotOutput("plot"),
tableOutput("minmax")
)
)
)
server = function(input, output, session) {
output$plot = renderPlot({
d |>
filter(name %in% input$name) |>
ggplot(aes(x=date, y=.data[[input$var]])) +
geom_line() +
theme_minimal()
})
output$minmax = renderTable({
d |>
filter(name %in% input$name) |>
mutate(
year = lubridate::year(date) |> as.integer()
) |>
summarize(
`min temp` = min(temp_min),
`max temp` = max(temp_max),
.by = year
)
})
}
shinyApp(ui = ui, server = server)demos/demo04.R
library(tidyverse)
library(shiny)
d = readr::read_csv(here::here("data/weather.csv"))
d_vars = c("Average temp" = "temp_avg",
"Min temp" = "temp_min",
"Max temp" = "temp_max",
"Total precip" = "precip",
"Snow depth" = "snow",
"Wind direction" = "wind_direction",
"Wind speed" = "wind_speed",
"Air pressure" = "air_press")
ui = fluidPage(
titlePanel("Weather Data"),
sidebarLayout(
sidebarPanel(
radioButtons(
"name", "Select an airport",
choices = c(
"Seattle-Tacoma",
"Raleigh-Durham",
"Houston Intercontinental",
"Denver",
"Los Angeles",
"John F. Kennedy"
)
),
selectInput(
"var", "Select a variable",
choices = d_vars, selected = "temp_avg"
)
),
mainPanel(
plotOutput("plot"),
tableOutput("minmax")
)
)
)
server = function(input, output, session) {
d_city = reactive({
d |>
filter(name %in% input$name)
})
output$plot = renderPlot({
d_city() |>
ggplot(aes(x=date, y=.data[[input$var]])) +
ggtitle(names(d_vars)[d_vars==input$var]) +
geom_line() +
theme_minimal()
})
output$minmax = renderTable({
d_city() |>
mutate(
year = lubridate::year(date) |> as.integer()
) |>
summarize(
`min temp` = min(temp_min),
`max temp` = max(temp_max),
.by = year
)
})
}
shinyApp(ui = ui, server = server)reactive() tipsExpressions are written in the same way as render*() functions
If react_obj = reactive({...}) then any consumer must access value using react_obj() and not react_obj
Like input reactive expressions may only be used within reactive contexts (e.g. render*(), reactive(), observer(), etc.)
Their primary use is similar to a function in an R script, they help to
avoid repeating ourselves
decompose complex computations into smaller / more modular steps
improve computational efficiency by breaking up / simplifying reactive dependencies
With these additions, what should our reactive graph look like now?
observer()These are constructed in the same way as a reactive() however an observer does not return a value, instead they are used for their side effects.
The side effects in most cases involve sending data to the client broswer, e.g. updating a UI element
While not obvious given their syntax - the results of the render*() functions are observers.
demos/demo05.R
library(tidyverse)
library(shiny)
d = readr::read_csv(here::here("data/weather.csv"))
d_vars = c("Average temp" = "temp_avg",
"Min temp" = "temp_min",
"Max temp" = "temp_max",
"Total precip" = "precip",
"Snow depth" = "snow",
"Wind direction" = "wind_direction",
"Wind speed" = "wind_speed",
"Air pressure" = "air_press")
ui = fluidPage(
titlePanel("Weather Data"),
sidebarLayout(
sidebarPanel(
selectInput(
"region", "Select a region",
choices = c("West", "Midwest", "Northeast", "South")
),
selectInput(
"name", "Select an airport", choices = c()
),
selectInput(
"var", "Select a variable",
choices = d_vars, selected = "temp_avg"
)
),
mainPanel(
plotOutput("plot"),
tableOutput("minmax")
)
)
)
server = function(input, output, session) {
observe({
updateSelectInput(
session, "name",
choices = d |>
distinct(region, name) |>
filter(region == input$region) |>
pull(name)
)
})
d_city = reactive({
d |>
filter(name %in% input$name)
})
output$plot = renderPlot({
d_city() |>
ggplot(aes(x=date, y=.data[[input$var]])) +
ggtitle(names(d_vars)[d_vars==input$var]) +
geom_line() +
theme_minimal()
})
output$minmax = renderTable({
d_city() |>
mutate(
year = lubridate::year(date) |> as.integer()
) |>
summarize(
`min temp` = min(temp_min),
`max temp` = max(temp_max),
.by = year
)
})
}
shinyApp(ui = ui, server = server)With these additions, what should our reactive graph look like now?
req()
region but no initial selection for name because of this we have some warnings generated in the console:Warning: There were 2 warnings in `summarize()`.
The first warning was:
ℹ In argument: `min temp = min(temp_min)`.
Caused by warning in `min()`:
! no non-missing arguments to min; returning Inf
ℹ Run dplyr::last_dplyr_warnings() to see the 1 remaining warning.This can be a common occurrence with Shiny, particularly at initialisation or when a user enters bad / unexpected input(s).
A good way to protect against this is to validate your inputs - the simplest way is to use req() which checks if a value is truthy.
Non-truthy values prevent further execution of the reactive code (and downstream consumer’s code).
Using the code provided in exercise/ex04.R (based on demo/demo05.R) as a starting point add the calls to req() necessary to avoid the initialisation warnings.
Also, think about if there are any other locations in our app where req() might be useful.
Hint - thinking about how events “flow” through the reactive graph will be helpful here.
10:00